Comparative Evaluation of Deep Traffic Sign Classification Models Under Visual Degradations and Interpretability Analysis

Engineering notes

All models were trained from scratch on a fixed stratified split of the German Traffic Sign Recognition Benchmark (GTSRB) using five independent random seeds. The evaluation considered clean classification performance, training stability, bootstrap confidence intervals, McNemar paired tests, probabilistic calibration, severity-wise robustness under blur, central occlusion, low-light, and Gaussian noise corruptions, external validation on 360 cropped German Traffic Sign Detection Benchmark (GTSDB) signs, computational efficiency, and Grad-CAM-based diagnostic analysis.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Reliable traffic sign classification is essential for advanced driver assistance and autonomous-driving systems because recognition errors under real road conditions can directly affect navigation, warning, and safety-related decisions. This study presents a reliability-oriented comparison of BaselineCNN, ResNet18, MobileNetV2, MobileNetV2 enhanced with the Convolutional Block Attention Module (CBAM), and knowledge-distilled MobileNetV2. All models were trained from scratch on a fixed stratified split of the German Traffic Sign Recognition Benchmark (GTSRB) using five independent random seeds. The evaluation considered clean classification performance, training stability, bootstrap confidence intervals, McNemar paired tests, probabilistic calibration, severity-wise robustness under blur, central occlusion, low-light, and Gaussian noise corruptions, external validation on 360 cropped German Traffic Sign Detection Benchmark (GTSDB) signs, computational efficiency, and Grad-CAM-based diagnostic analysis. Across five seeds, ResNet18 achieved the strongest mean clean performance, with an accuracy of 0.9856 ± 0.0093 and macro-F1 of 0.9817 ± 0.0134. MobileNetV2 remained competitive, with an accuracy of 0.9813 ± 0.0057 and macro-F1 of 0.9773 ± 0.0069, whereas BaselineCNN was substantially weaker, with an accuracy of 0.8459 ± 0.0165 and macro-F1 of 0.8384 ± 0.0189. ResNet18 also showed strong calibration, with an expected calibration error of 0.0017, and achieved the best GTSDB macro-F1 of 0.9389 in the representative Seed-42 external evaluation. Severe central occlusion was the most damaging corruption, reducing all models below 0.11 macro-F1, while low-light degradation was comparatively less harmful for the stronger classifiers. The results show that model ranking changes across accuracy, calibration, robustness, external transfer, computational cost, and visual diagnostic behavior. Therefore, traffic sign classifiers should be selected using multi-seed, multi-metric evaluation rather than clean benchmark accuracy alone.

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